Towards Monosemanticity: Decomposing Language Models With Dictionary Learning

Mechanistic interpretability seeks to understand neural networks by breaking them into components that are more easily understood than the whole. By understanding the function of each component, and how they interact, we hope to be able to reason about the behavior of the entire network. The first step in that program is to identify the correct components to analyze.

Unfortunately, the most natural computational unit of the neural network – the neuron itself – turns out not to be a natural unit for human understanding. This is because many neurons are polysemantic: they respond to mixtures of seemingly unrelated inputs. In the vision model Inception v1, a single neuron responds to faces of cats and fronts of cars . In a small language model we discuss in this paper, a single neuron responds to a mixture of academic citations, English dialogue, HTTP requests, and Korean text. Polysemanticity makes it difficult to reason about the behavior of the network in terms of the activity of individual neurons.

One potential cause of polysemanticity is superposition , a hypothesized phenomenon where a neural network represents more independent "features" of the data than it has neurons by assigning each feature its own linear combination of neurons. If we view each feature as a vector over the neurons, then the set of features form an overcomplete linear basis for the activations of the network neurons. In our previous paper on Toy Models of Superposition , we showed that superposition can arise naturally during the course of neural network training if the set of features useful to a model are sparse in the training data. As in compressed sensing, sparsity allows a model to disambiguate which combination of features produced any given activation vector.For more discussion of this point, see Distributed Representations: Composition and Superposition.

In Toy Models of Superposition, we described three strategies to finding a sparse and interpretable set of features if they are indeed hidden by superposition: (1) creating models without superposition, perhaps by encouraging activation sparsity; (2) using dictionary learning to find an overcomplete feature basis in a model exhibiting superposition; and (3) hybrid approaches relying on a combination of the two. Since the publication of that work, we've explored all three approaches. We eventually developed counterexamples which persuaded us that the sparse architectural approach (approach 1) was insufficient to prevent polysemanticity, and that standard dictionary learning methods (approach 2) had significant issues with overfitting.

In this paper, we use a weak dictionary learning algorithm called a sparse autoencoder to generate learned features from a trained model that offer a more monosemantic unit of analysis than the model's neurons themselves. Our approach here builds on a significant amount of prior work, especially in using dictionary learning and related methods on neural network activations (e.g. ), and a more general allied literature on disentanglement. We also note interim reports which independently investigated the sparse autoencoder approach in response to Toy Models, culminating in the recent manuscript of Cunningham et al. .

The goal of this paper is to provide a detailed demonstration of a sparse autoencoder compellingly succeeding at the goals of extracting interpretable features from superposition and enabling basic circuit analysis. Concretely, we take a one-layer transformer with a 512-neuron MLP layer, and decompose the MLP activations into relatively interpretable features by training sparse autoencoders on MLP activations from 8 billion data points, with expansion factors ranging from 1× (512 features) to 256× (131,072 features). We focus our detailed interpretability analyses on the 4,096 features learned in one run we call A/1.

This report has four major sections. In Problem Setup, we provide motivation for our approach and describe the transformers and sparse autoencoders we train. In Detailed Investigations of Individual Features, we offer an existence proof – we make the case that several features we find are functionally specific causal units which don't correspond to neurons. In Global Analysis, we argue that the typical feature is interpretable and that they explain a non-trivial portion of the MLP layer. Finally, in Phenomenology we describe several properties of our features, including feature-splitting, universality, and how they can form "finite state automata"-like systems implementing interesting behaviors.

We also provide three comprehensive visualizations of features. First, for all features from 90 learned dictionaries we present activating dataset examples and downstream logit effects. We recommend the reader begin with the visualization of A/1. Second, we provide a data-oriented view, showing all features active on each token of 25 texts. Finally, we coembed all 4,096 features from A/1 and all 512 features from A/0 into the plane using UMAP to allow for interactive exploration of the space of features: